CAREER: Understanding metal/support interactions in catalysis with statistical learning

职业：通过统计学习了解催化中金属/载体的相互作用

• 批准号: 2143941
• 负责人: Thomas Senftle
• 金额: $ 57.21万
• 依托单位: William Marsh Rice University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2027-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2143941&HistoricalAwards=false
• 关键词: CAREER; Understanding; metal; support; interactions

Heterogeneous catalysts consisting of metal nanoparticles supported on oxide surfaces are essential for promoting rapid and energy efficient manufacturing of fuels and chemicals, as well as controlling emissions of environmental pollutants. Catalyst research and development has historically relied primarily on experimental methods – often involving time- and resource-intensive materials screening. In recent years, however, increasing effort has been directed toward predictive design of advanced catalyst technologies, enabled by advances in theory, computational methods, and artificial intelligence. The project develops and applies advanced simulation tools, based on machine learning, which will be used to establish strategies for controlling metal/support interactions to enhance catalyst stability, activity, and selectivity. Models built with these tools will accelerate research efforts to design new types of catalysts consisting of either isolated metal atoms or small clusters dispersed on high-surface area metal oxide supports. In addition to improving catalyst reactivity, product selectivity, and stability, the project will enable discovery and design of catalysts that reduce the amount of expensive and strategic metals (such as platinum, palladium, and rhodium) used widely in chemical manufacturing and pollution control. Beyond improving catalytic technologies, the project will extend educational efforts by establishing an outreach program for underserved communities through the Tapia Center for Excellence and Equity in Education at Rice University. Synergistic interactions between metal nanoparticles and oxide supports are known to influence catalyst performance. The goal of the project is to develop a theoretical framework that uses statistical-based machine learning, together with density functional theory, to understand metal/support interactions (MSIs) in heterogeneous catalysis. The first objective is to apply computationally efficient statistical learning methodologies to identify the physical descriptors of metal binding on oxide supports, which in turn can be used to predict metal sintering rates and cluster morphology. The second objective is to predict catalytic activity and selectivity by constructing models that relate MSIs to adsorbate binding energies and the associated kinetic barriers that control mechanisms for prototypical model reactions, such as carbon monoxide oxidation. The third objective is to apply thermodynamic analyses to understand the stability of support modifications that are strategically introduced to enhance MSIs. Thus, the overall methodology not only will identify desirable surface modifications for controlling catalytic behavior by tuning MSIs, but also will predict the environments in which such modifications can be achieved most readily.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

由金属纳米颗粒负载在氧化物表面的多相催化剂对于促进快速、高效地制造燃料和化学品以及控制环境污染物的排放至关重要。催化剂的研究和开发历来主要依赖于实验方法--通常涉及耗时和资源密集型的材料筛选。然而，近年来，由于理论、计算方法和人工智能的进步，人们越来越多地致力于先进催化剂技术的预测设计。该项目开发和应用基于机器学习的先进模拟工具，将用于建立控制金属/载体相互作用的策略，以提高催化剂的稳定性、活性和选择性。用这些工具建立的模型将加速研究工作，以设计由孤立的金属原子或分散在高比表面积金属氧化物载体上的小团簇组成的新型催化剂。除了提高催化剂的反应性、产品选择性和稳定性外，该项目还将使发现和设计的催化剂能够减少化学制造和污染控制中广泛使用的昂贵和战略金属(如铂、钯和铑)的数量。除了改进催化技术，该项目还将通过莱斯大学塔皮亚卓越和公平教育中心为服务不足的社区建立一个外联计划，从而扩大教育努力。金属纳米颗粒和氧化物载体之间的协同作用会影响催化剂的性能。该项目的目标是开发一种理论框架，使用基于统计的机器学习和密度泛函理论来理解多相催化中的金属/载体相互作用(MSI)。第一个目标是应用计算高效的统计学习方法来识别金属在氧化物载体上结合的物理描述符，这反过来可以用来预测金属烧结速度和团簇形态。第二个目标是通过构建模型来预测催化活性和选择性，该模型将MSI与吸附结合能和相关的动力学势垒联系起来，这些动力学势垒控制着原型模型反应的机理，如一氧化碳氧化。第三个目标是应用热力学分析来了解为增强MSI而战略性引入的支撑件的稳定性。因此，总体方法不仅将确定通过调整MSI来控制催化行为的理想表面修饰，还将预测最容易实现此类修饰的环境。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。